{
 "nbformat": 4,
 "nbformat_minor": 0,
 "metadata": {
  "colab": {
   "provenance": [],
   "machine_shape": "hm",
   "gpuType": "T4"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  },
  "language_info": {
   "name": "python"
  },
  "accelerator": "GPU"
 },
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# 数据集导入(预制链接)"
   ],
   "metadata": {
    "id": "kBRw5QHhBkax"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 官方版本数据导入"
   ],
   "metadata": {
    "id": "gaD7ugivEL2R"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hfHjWRgKBR47",
    "outputId": "04e8d90d-2b4b-40b2-e428-bdfccc33899a"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "--2024-07-14 08:11:01--  https://drive.usercontent.google.com/download?id=1JwR0Q1ArTg6c47EF2ZuIBpQwCPgXKrO2&export=download&authuser=0&confirm=t&uuid=dc3aa13c-c3a9-458f-983a-8586798cb635&at=APZUnTX25XMxi-z-3wBcgR93IGsL%3A1719235792953\n",
      "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 172.253.118.132, 2404:6800:4003:c05::84\n",
      "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|172.253.118.132|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1084182095 (1.0G) [application/octet-stream]\n",
      "Saving to: ‘Dataset.zip’\n",
      "\n",
      "Dataset.zip         100%[===================>]   1.01G  44.6MB/s    in 27s     \n",
      "\n",
      "2024-07-14 08:11:31 (38.1 MB/s) - ‘Dataset.zip’ saved [1084182095/1084182095]\n",
      "\n",
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      "  inflating: Dataset/Training_data/train_pressure_min_std.txt  \n",
      "  inflating: Dataset/Training_data/watertight_global_bounds.txt  \n",
      "  inflating: Dataset/Training_data/watertight_meshes.txt  \n"
     ]
    }
   ],
   "source": [
    "!wget --header=\"Host: drive.usercontent.google.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6\" --header=\"Cookie: __Secure-ENID=12.SE=Yd0Bj-CLJ14fnd4qzdJHmwUs4B5zz46UaPC1cPJigNqqFV9PtM2CYyBpSbCkOyzUwzlEdZ1nZFf-igtGi7wSdJ_gqQSfQfh84r9egqFQAy9-GKayCRbdQKdera-2mkpuIT-c64CyR9vfNojM3hxZ9Dej-dGvtxlGjal9ttEHybw; __gsas=ID=ae0421b9a34b478c:T=1710758437:RT=1710758437:S=ALNI_MZP13R9ZOHbCzC0rgHSMrGXj6GCsg; HSID=A-4I-ZudDNUIB6EKH; SSID=A7v_1v9un6xAwVNku; APISID=ctK8IbLjeuDUmgys/AFnMSLWt9KddceDI6; SAPISID=J7GhTwED67EBqJJT/A9nwK7mr0ijGPw08r; __Secure-1PAPISID=J7GhTwED67EBqJJT/A9nwK7mr0ijGPw08r; __Secure-3PAPISID=J7GhTwED67EBqJJT/A9nwK7mr0ijGPw08r; SID=g.a000kgiBabgKCiCYKve9zfoWVgz9eu8sBA6N4XDPPpP5pcW16_C_kzuBV1TvOhAIC8VF1e9fpgACgYKATQSARQSFQHGX2Mi8LXUwWoIwNCEPU8Sy3mXUxoVAUF8yKqGXVfjTGz9gQal7nwGr4Pl0076; __Secure-1PSID=g.a000kgiBabgKCiCYKve9zfoWVgz9eu8sBA6N4XDPPpP5pcW16_C_PDa-DzVmbdGFPyxMQpk9_QACgYKAewSARQSFQHGX2MiAeee4fn0OWglWZfAygqkyBoVAUF8yKp-Sfmtnueimxc-0QbJRF9I0076; __Secure-3PSID=g.a000kgiBabgKCiCYKve9zfoWVgz9eu8sBA6N4XDPPpP5pcW16_C_g9IrMeU98APBo9Stp6wEnAACgYKAQASARQSFQHGX2MiFWtc9ucONXnpxBzlRdudEhoVAUF8yKoeZwCpJDnjfAFjGssHSUGm0076; NID=515=GQhY9nKKFCx3qFDjE0MA4ubjWNdef6xCIY_RfWOPWKEtyfBN3nAUl8WHI2VczjNQ4rVkj1XBAY8WNWHXyqSK10CfT4FxsFlPzrHIJpeTtm1nWRNBd9AAfBKJHz4XpESszntVUTE_59RklZuKo0vk1poReVi2da1PZKC3CTKH2Ll3gB5xuB9wf4bmq8ylVUuIROPJczr0XnCuUHV3qLdBvgy9_870b6UwOq1iOlIxFQFm01EZ4pqF4q1Ub3QRSWpEMLh4LSZFpJ5O255R5OV7krmEdDvH_sHoTEPZAg2PoEpwAyGK6Xp9qcLIlldgx5-5V86N8Wtb93uTlQuA_CFXb5_2eP3bgeX8txwlJ5SrldVjg9ctzYtBU2RwJKTSvdHfIG7lpOkg6XlkvDOcJpR3DihT_OlqnPn7drCAJpvVDv29hZn5XPMXaSrNdbG64OJ9urJEw5odEwsLYkkpC1vmlUcuoo52S5f6RQu0Z8kZiV8iRW6XIqHsSmQHunVaxk6xWCStUg; __Secure-1PSIDTS=sidts-CjEB3EgAEtTS0OazynCofIH4RCBstiRP5flEcvYW3z4Fg9oGd5QOESDOZt1wO2iqUYHjEAA; __Secure-3PSIDTS=sidts-CjEB3EgAEtTS0OazynCofIH4RCBstiRP5flEcvYW3z4Fg9oGd5QOESDOZt1wO2iqUYHjEAA; SIDCC=AKEyXzVI6aMX8lSDja86Yts3FBAtBzPCzVNgaX5BCz78NWsWzlT3yFWKUV7ZE46SFzE1GiBI-cHdTw; __Secure-1PSIDCC=AKEyXzUo4NQAwqqPMxP2eye-MFEbZmBIm_sZqRU1amttg0YoQkc8ZKSNXdHl5jNCMEbhrUHhS9-K; __Secure-3PSIDCC=AKEyXzWf2lIdmDLeZKpXSi9GytVQb6XudrYiNUBA5gW952YuLh8kL6T3IbBlu8zOTfGEcdUp5O1R\" --header=\"Connection: keep-alive\" \"https://drive.usercontent.google.com/download?id=1JwR0Q1ArTg6c47EF2ZuIBpQwCPgXKrO2&export=download&authuser=0&confirm=t&uuid=dc3aa13c-c3a9-458f-983a-8586798cb635&at=APZUnTX25XMxi-z-3wBcgR93IGsL%3A1719235792953\" -c -O 'Dataset.zip'\n",
    "!unzip Dataset.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "id": "ITzT8s2wgZG0"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "!mkdir Dataset/data_test_A/\n",
    "!mv Dataset/Testset_track_A/Inference/* Dataset/data_test_A/\n",
    "!mv watertight_meshes.txt Dataset/data_test_A/\n",
    "!pip install plyfile -i https://pypi.tuna.tsinghua.edu.cn/simple"
   ],
   "metadata": {
    "id": "-xXUEozBUJVZ"
   },
   "execution_count": 7,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "!pip install loguru"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FOvVhsX6VtWy",
    "outputId": "0a8bf5a1-404c-47ae-be73-76addbe8feb9"
   },
   "execution_count": 20,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Collecting loguru\n",
      "  Downloading loguru-0.7.2-py3-none-any.whl (62 kB)\n",
      "\u001B[?25l     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m0.0/62.5 kB\u001B[0m \u001B[31m?\u001B[0m eta \u001B[36m-:--:--\u001B[0m\r\u001B[2K     \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m62.5/62.5 kB\u001B[0m \u001B[31m2.6 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\n",
      "\u001B[?25hInstalling collected packages: loguru\n",
      "Successfully installed loguru-0.7.2\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]='0'\n",
    "\n",
    "import numpy as np\n",
    "import random\n",
    "import torch\n",
    "\n",
    "path='data/'\n",
    "version='L4090-A004'\n",
    "\n",
    "os.makedirs(path+'models',exist_ok=True)\n",
    "os.makedirs(path+'feature',exist_ok=True)\n",
    "os.makedirs(path+'feature_importance',exist_ok=True)\n",
    "os.makedirs(path+'submissions',exist_ok=True)\n",
    "os.makedirs(path+'submissions/content/gen_answer_A/',exist_ok=True)\n",
    "os.makedirs(path+'logs',exist_ok=True)"
   ],
   "metadata": {
    "id": "MTKXLAnp9Kdx"
   },
   "execution_count": 3,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "def seed_everything(seed=42):\n",
    "    random.seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "seed_everything()"
   ],
   "metadata": {
    "id": "mqL2U0-JT09Z"
   },
   "execution_count": 4,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "train_df=pd.read_csv(\"Dataset/Training_data/watertight_meshes.txt\",header=None,dtype=str)\n",
    "train_df.columns=['id']\n",
    "train_df=train_df.sort_values(by=['id'],ascending=[True]).reset_index(drop=True)\n",
    "train_df"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "yL4zfytyT3AT",
    "outputId": "69aec662-cee8-436d-b1b6-10f932be9cb2"
   },
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      id\n",
       "0    001\n",
       "1    002\n",
       "2    004\n",
       "3    005\n",
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    "test_df=pd.read_csv(\"Dataset/data_test_A/watertight_meshes.txt\",header=None,dtype=str)\n",
    "test_df.columns=['id']\n",
    "test_df=test_df.sort_values(by=['id'],ascending=[True]).reset_index(drop=True)\n",
    "test_df"
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       "      <td>691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>722</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
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       "        document.querySelector('#df-4309ce0e-51bc-4967-96ab-b769b19decd1 button.colab-df-convert');\n",
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       "                                                    [key], {});\n",
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       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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       "  .colab-df-quickchart-complete:disabled,\n",
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       "    background-color: var(--disabled-bg-color);\n",
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       "    border: 2px solid var(--fill-color);\n",
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       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "  }\n",
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       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
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       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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       "\n",
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       "\n",
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       "      [theme=dark] .colab-df-generate:hover {\n",
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       "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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       "      }\n",
       "    </style>\n",
       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('test_df')\"\n",
       "            title=\"Generate code using this dataframe.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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       "        document.querySelector('#id_7525c81c-e30f-429c-a42e-a91a0e4b146c button.colab-df-generate');\n",
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       "        google.colab.notebook.generateWithVariable('test_df');\n",
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       "\n",
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       "  </div>\n"
      ],
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "dataframe",
       "variable_name": "test_df",
       "summary": "{\n  \"name\": \"test_df\",\n  \"rows\": 50,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 50,\n        \"samples\": [\n          \"675\",\n          \"709\",\n          \"696\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
      }
     },
     "metadata": {},
     "execution_count": 9
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "from plyfile import PlyData\n",
    "from tqdm import tqdm\n",
    "train_pos=[]\n",
    "train_press=[]\n",
    "for file_id in tqdm(train_df['id']):\n",
    "    ply = PlyData.read(f\"Dataset/Training_data/Feature/mesh_{file_id}.ply\")\n",
    "    vtx = ply['vertex']\n",
    "    x=np.array(vtx['x']).reshape(1,3586,1)\n",
    "    y=np.array(vtx['y']).reshape(1,3586,1)\n",
    "    z=np.array(vtx['z']).reshape(1,3586,1)\n",
    "    pos=np.concatenate([x,y,z],axis=-1)\n",
    "    train_pos.append(pos)\n",
    "    press = np.load(f\"Dataset/Training_data/Label/press_{file_id}.npy\").reshape((-1,))\n",
    "    press = np.concatenate((press[0:16], press[112:]), axis=0).reshape(1,-1)\n",
    "    train_press.append(press)\n",
    "train_pos=np.concatenate(train_pos,axis=0).astype(np.float32)\n",
    "train_press=np.concatenate(train_press,axis=0).astype(np.float32)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0GXtZpOQT38h",
    "outputId": "d8c9a744-99ff-477c-f37c-3201d9fda9e8"
   },
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|██████████| 500/500 [00:29<00:00, 16.70it/s]\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "test_pos=[]\n",
    "test_press=[]\n",
    "for file_id in tqdm(test_df['id']):\n",
    "    ply = PlyData.read(f\"Dataset/data_test_A/mesh_{file_id}.ply\")\n",
    "    vtx = ply['vertex']\n",
    "    x=np.array(vtx['x']).reshape(1,3586,1)\n",
    "    y=np.array(vtx['y']).reshape(1,3586,1)\n",
    "    z=np.array(vtx['z']).reshape(1,3586,1)\n",
    "    pos=np.concatenate([x,y,z],axis=-1)\n",
    "    test_pos.append(pos)\n",
    "test_pos=np.concatenate(test_pos,axis=0).astype(np.float32)\n"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0YaBLGq0UtyV",
    "outputId": "1fbf83b0-566a-4b06-9631-ad74644a3c8a"
   },
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|██████████| 50/50 [00:02<00:00, 20.21it/s]\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "mean,std=(-37.09, 48.0955)\n",
    "mean,std"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "J5ci86pmAbrM",
    "outputId": "b443fa3d-2903-4002-9105-bb14a4738fdf"
   },
   "execution_count": 12,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(-37.09, 48.0955)"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "train_press=(train_press-mean)/std"
   ],
   "metadata": {
    "id": "IR-oWQomArX8"
   },
   "execution_count": 13,
   "outputs": []
  },
  {
   "metadata": {
    "id": "SkTWdadl8tXh"
   },
   "cell_type": "code",
   "outputs": [],
   "execution_count": 14,
   "source": [
    "from torch.utils.data import Dataset, DataLoader\n",
    "class MyDataset(Dataset):\n",
    "    def __init__(self, Input1 , labels):\n",
    "        self.Input1 = torch.tensor(Input1)\n",
    "        self.labels = torch.tensor(labels)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.Input1)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return self.Input1[index],self.labels[index]"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "test_dataset=MyDataset(test_pos,torch.zeros(test_pos.shape[0],train_press.shape[1]))"
   ],
   "metadata": {
    "id": "nEmc5uvUEdji",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "fd16b6d2-8c25-43aa-d73a-3e538f1097f5"
   },
   "execution_count": 15,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "<ipython-input-14-a55a3d3a4c85>:5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  self.labels = torch.tensor(labels)\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "test_loader=DataLoader(test_dataset, batch_size=16, shuffle=False)"
   ],
   "metadata": {
    "id": "Dzv1EoeYDcL3"
   },
   "execution_count": 16,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "from transformers import BertConfig\n",
    "from modeling_bert_SDPA import BertModel\n",
    "from torch import nn\n",
    "\n",
    "class CustomModel(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(CustomModel, self).__init__()\n",
    "        self.config = BertConfig()\n",
    "        self.config.hidden_dropout_prob = 0.0\n",
    "        self.config.attention_probs_dropout_prob = 0.0\n",
    "        self.config.hidden_size = 512\n",
    "        self.config.num_attention_heads = 4\n",
    "        self.config.num_hidden_layers = 12\n",
    "        self.config.max_position_embeddings=3586\n",
    "        self.config.intermediate_size=2048\n",
    "        self.config.vocab_size=1\n",
    "        self.bert = BertModel(self.config)\n",
    "        self.fc1=nn.Linear(3,self.config.hidden_size)\n",
    "        self.fc2=nn.Linear(self.config.hidden_size,1)\n",
    "\n",
    "\n",
    "    def forward(self, x):\n",
    "        x=self.fc1(x)\n",
    "        output = self.bert(inputs_embeds=x)[0]\n",
    "        output=self.fc2(output).squeeze(dim=-1)\n",
    "        return output\n",
    "\n"
   ],
   "metadata": {
    "id": "XLNZMxywPYe1"
   },
   "execution_count": 17,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "\n",
    "class EMA():\n",
    "    def __init__(self, model, decay):\n",
    "        self.model = model\n",
    "        self.decay = decay\n",
    "        self.shadow = {}\n",
    "        self.backup = {}\n",
    "\n",
    "    def register(self):\n",
    "        for name, param in self.model.named_parameters():\n",
    "            if param.requires_grad:\n",
    "                self.shadow[name] = param.data.clone()\n",
    "\n",
    "    def update(self):\n",
    "        for name, param in self.model.named_parameters():\n",
    "            if param.requires_grad:\n",
    "                assert name in self.shadow\n",
    "                new_average = (1.0 - self.decay) * param.data + self.decay * self.shadow[name]\n",
    "                self.shadow[name] = new_average.clone()\n",
    "\n",
    "    def apply_shadow(self):\n",
    "        for name, param in self.model.named_parameters():\n",
    "            if param.requires_grad:\n",
    "                assert name in self.shadow\n",
    "                self.backup[name] = param.data\n",
    "                param.data = self.shadow[name]\n",
    "\n",
    "    def restore(self):\n",
    "        for name, param in self.model.named_parameters():\n",
    "            if param.requires_grad:\n",
    "                assert name in self.backup\n",
    "                param.data = self.backup[name]\n",
    "        self.backup = {}\n"
   ],
   "metadata": {
    "id": "PK_IRye3DV0u"
   },
   "execution_count": 21,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "from loguru import logger\n",
    "\n",
    "logger.add(path+f\"logs/log_{version}.log\")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_R4ymI9BcKYb",
    "outputId": "70dccd43-3da1-4a5e-fcd3-24fee2fbc64a"
   },
   "execution_count": 22,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "metadata": {},
     "execution_count": 22
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "class LpLoss(nn.Module):\n",
    "    def __init__(self, d=2, p=2, size_average=True, reduction='mean'):\n",
    "        super(LpLoss, self).__init__()\n",
    "        # Dimension and Lp-norm type are positive\n",
    "        assert d > 0 and p > 0\n",
    "\n",
    "        self.d = d\n",
    "        self.p = p\n",
    "        self.reduction = reduction\n",
    "        self.size_average = size_average\n",
    "\n",
    "    def forward(self, x, y):\n",
    "        # Compute L2 norm of the difference and Lp norm of y\n",
    "        diff_norms = torch.norm(x - y, 2)\n",
    "        y_norms = torch.norm(y, self.p)\n",
    "\n",
    "        # Handle the reduction\n",
    "        if self.reduction == 'mean':\n",
    "            return torch.mean(diff_norms / y_norms)\n",
    "        elif self.reduction == 'sum':\n",
    "            return torch.sum(diff_norms / y_norms)\n",
    "        else:\n",
    "            return diff_norms / y_norms"
   ],
   "metadata": {
    "id": "4Xrju_8XWMkx"
   },
   "execution_count": 23,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "class valid_LpLoss(object):\n",
    "    def __init__(self, d=2, p=2, size_average=True, reduction=True):\n",
    "        super(valid_LpLoss, self).__init__()\n",
    "        # Dimension and Lp-norm type are postive\n",
    "        assert d > 0 and p > 0\n",
    "\n",
    "        self.d = d\n",
    "        self.p = p\n",
    "        self.reduction = reduction\n",
    "        self.size_average = size_average\n",
    "\n",
    "\n",
    "    def rel(self, x, y):\n",
    "        diff_norms = np.linalg.norm(x-y, 2)\n",
    "        y_norms = np.linalg.norm(y, self.p)\n",
    "\n",
    "        if self.reduction:\n",
    "            if self.size_average:\n",
    "                return np.mean(diff_norms / y_norms)\n",
    "            else:\n",
    "                return np.sum(diff_norms / y_norms)\n",
    "\n",
    "        return diff_norms / y_norms\n",
    "\n",
    "    def __call__(self, x, y):\n",
    "        return self.rel(x, y)"
   ],
   "metadata": {
    "id": "WxfeoTriWNRk"
   },
   "execution_count": 24,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "from torch.cuda import amp # 导入AMP模块\n",
    "from datetime import datetime\n",
    "from tqdm import tqdm\n",
    "import time\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "from sklearn.model_selection import StratifiedKFold, KFold\n",
    "import math,time\n",
    "import shutil\n",
    "\n",
    "# criterion = nn.BCEWithLogitsLoss().cuda()\n",
    "criterion = LpLoss(size_average=True).cuda()  # 使用L2范数作为损失函数\n",
    "EPOCHS=300\n",
    "scaler = amp.GradScaler()\n",
    "loss_fn = valid_LpLoss(size_average=True)\n",
    "# model Constructing\n",
    "# ========================================================\n",
    "kf = KFold(n_splits=6, shuffle=True, random_state=42)\n",
    "for fold, (train_idx, valid_idx) in enumerate(kf.split(train_df)):\n",
    "    if os.path.exists(f\"best_valid_checkpoin_{version}-{fold}.pt\"):\n",
    "        shutil.copyfile(f\"best_valid_checkpoin_{version}-{fold}.pt\",path+f\"models/best_valid_checkpoin_{version}-{fold}.pt\")\n",
    "        continue\n",
    "    model=CustomModel()\n",
    "    model = model.cuda()\n",
    "    model=torch.compile(model)\n",
    "    ema = EMA(model, 0.999)\n",
    "    ema.register()\n",
    "\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
    "    train_dataset=MyDataset(train_pos[train_idx],train_press[train_idx])\n",
    "    valid_dataset=MyDataset(train_pos[valid_idx],train_press[valid_idx])\n",
    "    train_loader=DataLoader(train_dataset, batch_size=6, shuffle=True)\n",
    "    valid_loader=DataLoader(valid_dataset, batch_size=16, shuffle=False)\n",
    "\n",
    "    time_list=[time.time()]\n",
    "    # model Training and Saving\n",
    "    # =========================================================\n",
    "    best_score = np.inf\n",
    "    early_stop=0\n",
    "    for epoch in range(EPOCHS):\n",
    "        model.train()\n",
    "        epoch_loss = 0.0\n",
    "        for input_embeds,label1 in train_loader:\n",
    "            optimizer.zero_grad()\n",
    "            input_embeds,label1 = input_embeds.cuda(),label1.cuda()\n",
    "            with amp.autocast():\n",
    "                modelOutput = ema.model(input_embeds)\n",
    "                loss = criterion(modelOutput, label1)\n",
    "            scaler.scale(loss).backward()    # loss缩放并反向转播\n",
    "            scaler.step(optimizer)    # 更新参数（自动unscaling）\n",
    "            scaler.update()    # 基于动态Loss Scale更新loss_scaling系数\n",
    "            epoch_loss += loss.item() * input_embeds.shape[0]\n",
    "            ema.update()\n",
    "\n",
    "        ema.apply_shadow()\n",
    "        # model Evaluating\n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            valid_preds=[]\n",
    "            valid_labels=[]\n",
    "            val_loss=0.0\n",
    "            for input_embeds,label1 in valid_loader:\n",
    "                input_embeds,label1 = input_embeds.cuda(),label1.cuda()\n",
    "                val_modelOutput = ema.model(input_embeds)\n",
    "                val_loss += criterion(val_modelOutput, label1).item()* input_embeds.shape[0]\n",
    "                valid_preds.append(val_modelOutput.cpu())\n",
    "                valid_labels.append(label1.cpu())\n",
    "\n",
    "            valid_preds=torch.cat(valid_preds,dim=0).numpy()*std+mean\n",
    "            valid_labels=torch.cat(valid_labels,dim=0).numpy()*std+mean\n",
    "            score=[]\n",
    "            for idx in range(len(valid_preds)) :\n",
    "                valid_modelOutput = valid_preds[idx]\n",
    "                label1=valid_labels[idx]\n",
    "                l2 = loss_fn(valid_modelOutput,label1)\n",
    "                score.append(l2)\n",
    "            mse = mean_squared_error(valid_labels, valid_preds)\n",
    "            rmse = math.sqrt(mse)\n",
    "            mae = mean_absolute_error(valid_labels, valid_preds)\n",
    "            score=np.array(score)\n",
    "            score=np.mean(score)\n",
    "            time_list.append(time.time())\n",
    "            logger.info(f\"Fold [{fold}] | Epoch [{epoch + 1}/{EPOCHS}] | Loss: {epoch_loss/len(train_dataset):.4f} | Valid loss: {val_loss/len(valid_dataset):.4f} | Valid mse: {mse:.5f} | Valid rmse: {rmse:.5f} | Valid mae: {mae:.5f} | Valid score: {score:.5f} | time: {time_list[-1]-time_list[-2]:.4f}\")\n",
    "\n",
    "            #  | lr : {scheduler.get_last_lr()}\n",
    "            if score < best_score:\n",
    "                # model saving\n",
    "                logger.info(\"model saved\")\n",
    "                best_score = score\n",
    "                checkpoint = {\n",
    "                    \"epoch\" : epoch,\n",
    "                    \"model_state_dict\" : ema.model.state_dict(),\n",
    "                    \"best_score\" : best_score,\n",
    "                    # \"optimizer_state_dict\" : optimizer.state_dict(),\n",
    "                    # \"scheduler_state_dict\" : scheduler.state_dict(),\n",
    "                }\n",
    "                torch.save(checkpoint, path+f\"models/best_valid_checkpoin_{version}-{fold}.pt\")\n",
    "                early_stop=0\n",
    "            else:\n",
    "                early_stop+=1\n",
    "\n",
    "        if early_stop>=50:\n",
    "            break\n",
    "        ema.restore()\n"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "K-lNF7h-WNYC",
    "outputId": "8d99643d-47df-4af9-c804-d1601c61d877"
   },
   "execution_count": 25,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "\u001B[32m2024-07-14 08:29:00.617\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36m<cell line: 18>\u001B[0m:\u001B[36m79\u001B[0m - \u001B[1mFold [0] | Epoch [1/1] | Loss: 1.2585 | Valid loss: 0.9888 | Valid mse: 2268.85156 | Valid rmse: 47.63246 | Valid mae: 29.15616 | Valid score: 0.77940 | time: 210.5026\u001B[0m\n",
      "\u001B[32m2024-07-14 08:29:00.621\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36m<cell line: 18>\u001B[0m:\u001B[36m84\u001B[0m - \u001B[1mmodel saved\u001B[0m\n",
      "\u001B[32m2024-07-14 08:31:41.489\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36m<cell line: 18>\u001B[0m:\u001B[36m79\u001B[0m - \u001B[1mFold [1] | Epoch [1/1] | Loss: 1.0353 | Valid loss: 1.0773 | Valid mse: 2722.98877 | Valid rmse: 52.18226 | Valid mae: 38.71878 | Valid score: 0.85040 | time: 159.1661\u001B[0m\n",
      "\u001B[32m2024-07-14 08:31:41.491\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36m<cell line: 18>\u001B[0m:\u001B[36m84\u001B[0m - \u001B[1mmodel saved\u001B[0m\n",
      "\u001B[32m2024-07-14 08:34:15.689\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36m<cell line: 18>\u001B[0m:\u001B[36m79\u001B[0m - \u001B[1mFold [2] | Epoch [1/1] | Loss: 1.1739 | Valid loss: 1.1460 | Valid mse: 3020.66479 | Valid rmse: 54.96057 | Valid mae: 39.98763 | Valid score: 0.90655 | time: 153.1172\u001B[0m\n",
      "\u001B[32m2024-07-14 08:34:15.691\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36m<cell line: 18>\u001B[0m:\u001B[36m84\u001B[0m - \u001B[1mmodel saved\u001B[0m\n",
      "\u001B[32m2024-07-14 08:36:51.157\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36m<cell line: 18>\u001B[0m:\u001B[36m79\u001B[0m - \u001B[1mFold [3] | Epoch [1/1] | Loss: 1.0872 | Valid loss: 0.9784 | Valid mse: 2187.10352 | Valid rmse: 46.76648 | Valid mae: 27.94411 | Valid score: 0.77732 | time: 154.4222\u001B[0m\n",
      "\u001B[32m2024-07-14 08:36:51.160\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36m<cell line: 18>\u001B[0m:\u001B[36m84\u001B[0m - \u001B[1mmodel saved\u001B[0m\n",
      "\u001B[32m2024-07-14 08:39:26.065\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36m<cell line: 18>\u001B[0m:\u001B[36m79\u001B[0m - \u001B[1mFold [4] | Epoch [1/1] | Loss: 1.1184 | Valid loss: 1.0743 | Valid mse: 2691.44824 | Valid rmse: 51.87917 | Valid mae: 34.32491 | Valid score: 0.85481 | time: 153.8150\u001B[0m\n",
      "\u001B[32m2024-07-14 08:39:26.069\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36m<cell line: 18>\u001B[0m:\u001B[36m84\u001B[0m - \u001B[1mmodel saved\u001B[0m\n",
      "\u001B[32m2024-07-14 08:42:03.655\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36m<cell line: 18>\u001B[0m:\u001B[36m79\u001B[0m - \u001B[1mFold [5] | Epoch [1/1] | Loss: 1.1201 | Valid loss: 0.9899 | Valid mse: 2246.96948 | Valid rmse: 47.40221 | Valid mae: 28.67718 | Valid score: 0.78604 | time: 155.9186\u001B[0m\n",
      "\u001B[32m2024-07-14 08:42:03.657\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36m<cell line: 18>\u001B[0m:\u001B[36m84\u001B[0m - \u001B[1mmodel saved\u001B[0m\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "from sklearn.model_selection import StratifiedKFold, KFold\n",
    "import math\n",
    "fold_num=6\n",
    "kf = KFold(n_splits=fold_num, shuffle=True, random_state=42)\n",
    "oof=np.zeros(train_press.shape)\n",
    "test_preds=np.zeros((test_df.shape[0],3586))\n",
    "criterion = nn.MSELoss().cuda()\n",
    "loss_fn = valid_LpLoss(size_average=True)\n",
    "for fold, (train_idx, valid_idx) in enumerate(kf.split(train_df)):\n",
    "    model=CustomModel()\n",
    "    model=torch.compile(model)\n",
    "    state_dict=torch.load( path+f\"models/best_valid_checkpoin_{version}-{fold}.pt\")['model_state_dict']\n",
    "    model.load_state_dict(state_dict)\n",
    "    model=model.cuda()\n",
    "    valid_dataset=MyDataset(train_pos[valid_idx],train_press[valid_idx])\n",
    "    valid_loader=DataLoader(valid_dataset, batch_size=16, shuffle=False)\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        valid_preds=[]\n",
    "        valid_labels=[]\n",
    "        val_loss=0.0\n",
    "        for input_embeds,label1 in valid_loader:\n",
    "            input_embeds,label1 = input_embeds.cuda(),label1.cuda()\n",
    "            val_modelOutput = model(input_embeds)\n",
    "            val_loss += criterion(val_modelOutput, label1).item()* input_embeds.shape[0]\n",
    "            valid_preds.append(val_modelOutput.cpu())\n",
    "            valid_labels.append(label1.cpu())\n",
    "\n",
    "        valid_preds=torch.cat(valid_preds,dim=0).numpy()*std+mean\n",
    "        valid_labels=torch.cat(valid_labels,dim=0).numpy()*std+mean\n",
    "        score=[]\n",
    "        for idx in range(len(valid_preds)) :\n",
    "            valid_modelOutput = valid_preds[idx]\n",
    "            label1=valid_labels[idx]\n",
    "            l2 = loss_fn(valid_modelOutput,label1)\n",
    "            score.append(l2)\n",
    "        mse = mean_squared_error(valid_labels, valid_preds)\n",
    "        rmse = math.sqrt(mse)\n",
    "        mae = mean_absolute_error(valid_labels, valid_preds)\n",
    "        score=np.array(score)\n",
    "        score=np.mean(score)\n",
    "        print(fold,mse,rmse,mae,score)\n",
    "        oof[valid_idx] += valid_preds\n",
    "        test_preds_tmp=[]\n",
    "        for input_embeds,label1 in test_loader:\n",
    "            input_embeds = input_embeds.cuda()\n",
    "            test_modelOutput = model(input_embeds)\n",
    "            test_preds_tmp.append(test_modelOutput.cpu())\n",
    "        test_preds_tmp=torch.cat(test_preds_tmp,dim=0).numpy()*std+mean\n",
    "        test_preds+=test_preds_tmp/fold_num"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YpwxD7ghWNa7",
    "outputId": "fb8e4696-2ed3-4d91-976b-cf64f55a1269"
   },
   "execution_count": 26,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0 2268.8516 47.632463325971294 29.156158 0.77939934\n",
      "1 2722.9888 52.18226489461003 38.718784 0.85039836\n",
      "2 3020.6648 54.96057491440455 39.987633 0.90654874\n",
      "3 2187.1035 46.76647854633701 27.944115 0.77731836\n",
      "4 2691.4482 51.87916963664222 34.324905 0.85481393\n",
      "5 2246.9695 47.40220967868349 28.677177 0.7860399\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "score=[]\n",
    "for idx in range(len(oof)) :\n",
    "    valid_modelOutput = oof[idx]\n",
    "    label1=train_press[idx]*std+mean\n",
    "    l2 = loss_fn(valid_modelOutput,label1)\n",
    "    score.append(l2)\n",
    "score=np.array(score)\n",
    "score=np.mean(score)\n",
    "score"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ti3-9QyeWNdo",
    "outputId": "a6f62044-4fc1-4eac-8680-919df7988c6a"
   },
   "execution_count": 27,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.8257096629132656"
      ]
     },
     "metadata": {},
     "execution_count": 27
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "for idx,(pred) in enumerate(test_preds):\n",
    "    file_id=test_df['id'][idx]\n",
    "    np.save(path+f'submissions/content/gen_answer_A/press_{file_id}.npy',pred)\n"
   ],
   "metadata": {
    "id": "6Z7QWfYRWNgT"
   },
   "execution_count": 28,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "import zipfile\n",
    "import time\n",
    "folder_path=path+f'submissions/content/gen_answer_A/'\n",
    "with zipfile.ZipFile('B_result.zip', 'w', zipfile.ZIP_DEFLATED) as zipf:\n",
    "    for root, _, files in os.walk(folder_path):\n",
    "        for file in files:\n",
    "            file_path = os.path.join(root, file)\n",
    "            zipf.write(file_path, 'content/gen_answer_A/' + os.path.basename(file_path))"
   ],
   "metadata": {
    "id": "slXIlsixWNjJ"
   },
   "execution_count": 30,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [],
   "metadata": {
    "id": "JXnDeG8xWNmE"
   },
   "execution_count": null,
   "outputs": []
  }
 ]
}
